Artificial neural networks (ANNs) have permeated various disciplinary
domains, ranging from bioinformatics to financial analytics, where their
application has become an indispensable facet of contemporary scientific
research endeavors. However, the inherent limitations of traditional neural
networks arise due to their relatively fixed network structures and activation
functions. 1, The type of activation function is single and relatively fixed,
which leads to poor "unit representation ability" of the network, and it is
often used to solve simple problems with very complex networks; 2, the network
structure is not adaptive, it is easy to cause network structure redundant or
insufficient. To address the aforementioned issues, this study proposes a novel
neural network called X-Net. By utilizing our designed Alternating
Backpropagation mechanism, X-Net dynamically selects appropriate activation
functions based on derivative information during training to enhance the
network's representation capability for specific tasks. Simultaneously, it
accurately adjusts the network structure at the neuron level to accommodate
tasks of varying complexities and reduce computational costs. Through a series
of experiments, we demonstrate the dual advantages of X-Net in terms of
reducing model size and improving representation power. Specifically, in terms
of the number of parameters, X-Net is only 3$\%$ of baselines on average, and
only 1.4$\%$ under some tasks. In terms of representation ability, X-Net can
achieve an average $R^2$=0.985 on the fitting task by only optimizing the
activation function without introducing any parameters. Finally, we also tested
the ability of X-Net to help scientific discovery on data from multiple
disciplines such as society, energy, environment, and aerospace, and achieved
concise and good results.

本研究提出了一种名为 X-Net 的新型神经网络，通过使用交替反向传播机制，在训练过程中根据导数信息动态选择适当的激活函数，增强网络对特定任务的表示能力。同时，在神经元级别准确调整网络结构以适应不同复杂度的任务和减少计算成本。通过一系列实验，我们展示了 X-Net 在减小模型大小和提高表示能力方面的双重优势。具体而言，就参数数量而言，X-Net 的平均值仅为基准的 3％，某些任务下仅为 1.4％。在表示能力方面，仅通过优化激活函数而不引入任何参数，X-Net 在拟合任务上可以实现平均 R^2=0.985。最后，我们还测试了 X-Net 在社会、能源、环境和航空航天等多个学科的数据上帮助科学发现的能力，并取得了简洁而优异的结果。

一种新的神经计算范式：具有可学习神经元和适应结构的 X-Net

A Novel Paradigm for Neural Computation: X-Net with Learnable Neurons  and Adaptable Structure

Learning robust multi-modal feature representations is critical for boosting
tracking performance. To this end, we propose a novel X Modality Assisting
Network (X-Net) to shed light on the impact of the fusion paradigm by
decoupling the visual object tracking into three distinct levels, facilitating
subsequent processing. Firstly, to tackle the feature learning hurdles stemming
from significant differences between RGB and thermal modalities, a
plug-and-play pixel-level generation module (PGM) is proposed based on
self-knowledge distillation learning, which effectively generates X modality to
bridge the gap between the dual patterns while reducing noise interference.
Subsequently, to further achieve the optimal sample feature representation and
facilitate cross-modal interactions, we propose a feature-level interaction
module (FIM) that incorporates a mixed feature interaction transformer and a
spatial-dimensional feature translation strategy. Ultimately, aiming at random
drifting due to missing instance features, we propose a flexible online
optimized strategy called the decision-level refinement module (DRM), which
contains optical flow and refinement mechanisms. Experiments are conducted on
three benchmarks to verify that the proposed X-Net outperforms state-of-the-art
trackers.

通过解耦可视目标跟踪为三个不同层次的组成部分，我们提出了一种名为 X-Net 的新型模态协助网络，用于学习鲁棒的多模态特征表示，解决 RGB 和热模态之间巨大差异带来的特征学习障碍，并改善跟踪性能。